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# first line: 239 |
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def Clustering(similarity): |
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similarityNP = np.array(similarity) |
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n_clusters = 25 # change that to send less diverse projections |
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kmedoids = KMedoids(n_clusters=n_clusters, random_state=0, metric='precomputed').fit(similarityNP) |
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global dataProc |
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clusterIndex = [] |
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for c in range(n_clusters): |
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cluster_indices = np.argwhere(kmedoids.labels_ == c).reshape(-1,) |
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D_c = similarityNP[cluster_indices][:, cluster_indices] |
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center = np.argmin(np.sum(D_c, axis=0)) |
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clusterIndex.append(cluster_indices[center]) |
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|
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return clusterIndex |
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@ -0,0 +1 @@ |
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@ -0,0 +1 @@ |
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